Real-Time Model Uncertainty in the United States: "Robust" Policies Put to the Test

I study forty-six vintages of FRB/US, the principal macro model used by the Federal Reserve, as measures of real-time model uncertainty and examine the robustness of commonly applied, simple monetary policy rules. Model uncertainty turns out to be a substantial problem: key model properties differ in important ways across model vintages, as do the optimized parameterizations of candidate rules. Among the simple monetary policy rules considered are rules that eschew feedback on the output gap, rules that target nominal income growth, and rules that allow for time variation in the equilibrium real interest rate. Many rules that previous research has shown to be robust in artificial economies would have failed to provide adequate stabilization in the real-time, realworld environment seen by the Federal Reserve staff. I identify certain policy rules that would have performed relatively well, and characterize their key features to draw more general lessons about the design of monetary policy under model uncertainty.